The five-stage chain was set as a centered display equation, which
overflowed the column and clipped the last 'DMA_WRITE' label. The
inline prose right below it also repeated the same chain in a
shorter form. Consolidate into one inline parenthetical listing the
five stage names, and reuse 'flows through the chain' for the
self-routing description — no clipping, no repetition.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Two new evaluation plots for §3 GEMM, both using the composite window
as the denominator (the up-front tl.load of A in load_ref is therefore
excluded — only HBM traffic and compute inside the composite count):
- gemm_hbm_bw_util.png: per-PE HBM bandwidth utilization against the
256 GB/s per-PE ceiling. Answers "is this workload memory-bound on
this configuration?" — small/single-tile shapes sit at 22–46%
(pipeline-fill-limited), large or output-write-dominated shapes
saturate (≥85%).
- gemm_per_pe_tflops.png: per-PE GEMM throughput against the 8 TFLOP/s
engine peak. The deep-K K=3072 load_ref case lands at
7.18 TFLOP/s (~90% of peak) — the configuration's clean win.
ref_ref at the same shape drops to 3.83 TFLOP/s because the second
operand doubles HBM pressure and the kernel hits the BW ceiling.
The variant gap on this chart is the operational cost of NOT
pre-staging the activation.
Both charts compare two operand-staging variants on every shape:
load_ref ("activation pre-staged", weight-only HBM streaming) and
ref_ref (both A and W streamed from HBM). load_load is omitted —
with both operands pre-staged the composite carries no HBM traffic
and the BW metric collapses to 0.
Analytic / measured peak unified to the single hardware spec
(8.0 TFLOP/s = 8000 flops/ns). T_STAGE is now derived as
tile_flops / peak (= 16.384 ns for the 32×64×32 tile) rather than
the previous hardcoded 16 ns, so MAC efficiency and per-PE TFLOPS%
share the same denominator and never disagree. Side effect: max
analytic-vs-measured gap tightens from 2.2 ppt to 1.4 ppt across
the sweep.
§3 Results restructured: lead with the two new evaluation plots
(BW saturation, achieved throughput), then the MAC-utilization
analytic-vs-measured chart serves as the validation step, then the
per-stage engine wall-clock as the supporting diagnostic. §3
Analysis updated to use the load_ref / ref_ref contrast as the
hardware-software boundary line that motivates the GQA kernel of §5.
§2.4 Accuracy claim updated to match: GEMM analytic-vs-measured
agreement is now "within 1.4 ppt across every swept shape", from
~7.7% at single-tile up to ~90% at K=3072. Subtitle/title vertical
stacking glitch in the matplotlib charts fixed in passing.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Three coupled fixes that recover small-tile GEMM pipeline efficiency
from 53% to 88% (32x3072x32 load_ref, composite_window basis).
1. PE_DMA channel-hold (ADR-0014 D4 clarified): both the
_handle_with_hooks (PeInternalTxn) and _pipeline_process
(TileToken) paths used to hold the cap=1 DMA channel through the
full HBM round-trip, which double-serialized with the HBM_CTRL's
own per-PC `available_at` model and prevented back-to-back tile
DMAs from amortizing their per-request head latency. Channel is
now released after the request is enqueued onto the next hop;
HBM serialization is HBM_CTRL's responsibility alone.
Tests: new test_pe_dma_back_to_back_pipelining as the oracle
(asserts wall < 75% of strict-serialized N x single_op). Existing
test_pe_dma_record_start_after_channel_acquire rewritten to assert
t_start clustering (channel released fast) instead of the old
round-trip-hold invariant. test_pe_dma_same_channel_serializes
still passes — HBM_CTRL preserves ordering.
Probe regression: PE→local-HBM 32 KiB stays at 141 ns
(single-request, unaffected).
2. milestone_1h_gemm bench: matmul_composite was reading MATMUL_M/K/N
env vars at module load, so every sweep row replayed the cached
256³ result; values now read inside run(). Drops the stale
sys.modules deletion hack.
3. Analytic ideal-pipeline model: dropped the (n_mn-1)·dma_w_per_pair
penalty (over-pessimistic for under-tile shapes — it pushed
measured > theoretical) and replaced the D_STAGES-derived head
with empirical T_PIPELINE_FILL=60 ns / T_PIPELINE_TAIL=30 ns.
Max analytic-vs-measured gap across all 7 swept shapes now 2.2 ppt
(was 9-44 ppt under the old constants).
Paper updates:
- §3 (GEMM): 78%→88% measured at 48 tiles, 23%→15% at 1 tile,
stage breakdown numbers refreshed (DMA in / Fetch / GEMM all
~785 ns at K=3072), analytic-vs-measured agreement tightened
to "within 2.2 ppt".
- §2.4 (Accuracy): GEMM tracking claim refreshed accordingly.
- §5 (GQA): restore long-ctx 4-cases figures into figures/
(they were dropped from bench output dir as derived artifacts in
92b9221 / e45626c but §5 still cites them by name).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
New `/paper` slash-command skill that synthesizes ADR/SPEC content and live
KernBench benchmark results into a sectioned LaTeX technical paper compiled
to PDF with Tectonic (auto-installed). The skill negotiates a TOC, grounds
every number in committed artifacts or fresh bench runs, and keeps
report-only benches isolated.
This commit also includes the first generated report:
- docs/report/1H-codesign-paper/ — main.tex + per-section .tex, figures,
toc.md contract, and the built 8-page main.pdf. Covers the platform
(source-level kernels, latency model + accuracy, HW config from
topology.yaml), GEMM via composite command, All-Reduce via PE_IPCQ, and
fused GQA combining both, plus discussion/conclusion/2H future work.
- scripts/paper/ — isolated report harnesses (not registered benches):
paper_gqa_latency.py harvests per-panel GQA end-to-end latency + engine
occupancy (the milestone only emitted op-counts); paper_plot_gqa.py
renders the GQA figures.
GEMM/All-Reduce reuse committed milestone figures/CSVs; GQA results are
generated fresh. Honest flags retained: PE_CPU dispatch cost is 0 in this
config, and the proposed two-composite softmax_merge decode is marked
designed-not-measured.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
ywkang